Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System

Ali Charara, Hatem Ltaief, Damien Gratadour, David E. Keyes, Arnaud Sevin, Ahmad Abdelfattah, Eric Gendron, Carine Morel, Fabrice Vidal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

The European Extremely Large Telescope project (E-ELT) is one of Europe's highest priorities in ground-based astronomy. ELTs are built on top of a variety of highly sensitive and critical astronomical instruments. In particular, a new instrument called MOSAIC has been proposed to perform multi-object spectroscopy using the Multi-Object Adaptive Optics (MOAO) technique. The core implementation of the simulation lies in the intensive computation of a tomographic reconstruct or (TR), which is used to drive the deformable mirror in real time from the measurements. A new numerical algorithm is proposed (1) to capture the actual experimental noise and (2) to substantially speed up previous implementations by exposing more concurrency, while reducing the number of floating-point operations. Based on the Matrices Over Runtime System at Exascale numerical library (MORSE), a dynamic scheduler drives all computational stages of the tomographic reconstruct or simulation and allows to pipeline and to run tasks out-of order across different stages on heterogeneous systems, while ensuring data coherency and dependencies. The proposed TR simulation outperforms asymptotically previous state-of-the-art implementations up to 13-fold speedup. At more than 50000 unknowns, this appears to be the largest-scale AO problem submitted to computation, to date, and opens new research directions for extreme scale AO simulations. © 2014 IEEE.
Original languageEnglish (US)
Title of host publicationSC14: International Conference for High Performance Computing, Networking, Storage and Analysis
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages262-273
Number of pages12
ISBN (Print)9781479955008
DOIs
StatePublished - Nov 2014

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